When to transfer: a dynamic domain adaptation method for effective knowledge transfer-Reference-Cited by-同舟云学术

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When to transfer: a dynamic domain adaptation method for effective knowledge transfer

Published:2022-07-23 Issue:11 Volume:13 Page:3491-3508
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Xie Xiurui,Cai Qing,Zhang Hongjie,Zhang Malu,Yang Zeheng,Liu Guisong^ORCID

Funder

Natural Science Foundation of Guangdong Province

Department of Science and Technology of Sichuan Province

Natural Science Foundation of Sichuan Province

Zhongshan Science and Technology Bureau

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-022-01608-5.pdf

Reference61 articles.

1. Russakovsky O, Deng J, Hao S, Krause J, Satheesh S, Ma S, Huang Zhiheng, Karpathy A, Khosla A, Bernstein M (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

2. Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision Springer, Berlin, Heidelberg, pp 213–226

3. Sinno JP, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

4. Sun G, Ayepah-Mensah D, Xu R, Agbesi VK, Liu G, Jiang W (2021) Transfer learning for autonomous cell activation based on relational reinforcement learning with adaptive reward. IEEE Syst J 16:1044–1055

5. Hu X, Pan J, Li P, Li H, Wei H, Zhang Y (2016) Multi-bridge transfer learning. Knowl-Based Syst 97(C):60–74

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